{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>基于天气数据集的XGBoost分类实战</h1>Author:dsy Time:2022-01-21</center>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#导入需要用到的数据集\n",
    "!curl -O https://tianchi-media.oss-cn-beijing.aliyuncs.com/DSW/7XGBoost/train.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 函数库导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  基础函数库\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "\n",
    "## 绘图函数库\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 数据读取/载入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012/1/19</td>\n",
       "      <td>MountGinini</td>\n",
       "      <td>12.1</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>W</td>\n",
       "      <td>30.0</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015/4/13</td>\n",
       "      <td>Nhil</td>\n",
       "      <td>10.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>39.0</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1021.9</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010/8/5</td>\n",
       "      <td>Nuriootpa</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>W</td>\n",
       "      <td>28.0</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1025.9</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013/3/18</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>13.2</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>44.0</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011/2/16</td>\n",
       "      <td>Sale</td>\n",
       "      <td>14.1</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>E</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>92.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>1014.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013/4/16</td>\n",
       "      <td>Walpole</td>\n",
       "      <td>16.1</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1014.7</td>\n",
       "      <td>1013.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016/8/12</td>\n",
       "      <td>Albany</td>\n",
       "      <td>8.5</td>\n",
       "      <td>16.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011/8/7</td>\n",
       "      <td>Moree</td>\n",
       "      <td>10.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NNE</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>1015.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009/9/3</td>\n",
       "      <td>Bendigo</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WNW</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1012.3</td>\n",
       "      <td>1008.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014/10/15</td>\n",
       "      <td>NorfolkIsland</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>ENE</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1022.8</td>\n",
       "      <td>1021.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>18.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
       "0   2012/1/19    MountGinini     12.1     23.1       0.0          NaN   \n",
       "1   2015/4/13           Nhil     10.2     24.7       0.0          NaN   \n",
       "2    2010/8/5      Nuriootpa     -0.4     11.0       3.6          0.4   \n",
       "3   2013/3/18       Adelaide     13.2     22.6       0.0         15.4   \n",
       "4   2011/2/16           Sale     14.1     28.6       0.0          6.6   \n",
       "5   2013/4/16        Walpole     16.1     21.6       0.0          NaN   \n",
       "6   2016/8/12         Albany      8.5     16.4       5.0          2.2   \n",
       "7    2011/8/7          Moree     10.8     19.4       0.0          3.8   \n",
       "8    2009/9/3        Bendigo      9.0     20.6       0.0          4.0   \n",
       "9  2014/10/15  NorfolkIsland     14.0     21.1       0.0          6.6   \n",
       "\n",
       "   Sunshine WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  \\\n",
       "0       NaN           W           30.0          N  ...        60.0   \n",
       "1       NaN           E           39.0          E  ...        63.0   \n",
       "2       1.6           W           28.0          N  ...        97.0   \n",
       "3      11.0          SE           44.0          E  ...        47.0   \n",
       "4       6.7           E           28.0         NE  ...        92.0   \n",
       "5       NaN           E           26.0        NaN  ...        94.0   \n",
       "6       NaN         NaN            NaN         NW  ...        78.0   \n",
       "7       1.2         NNE           33.0          N  ...        67.0   \n",
       "8       NaN         WNW           48.0        NNE  ...        54.0   \n",
       "9       6.2         ENE           37.0         NE  ...        58.0   \n",
       "\n",
       "   Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
       "0         54.0          NaN          NaN       NaN       NaN     17.0   \n",
       "1         33.0       1021.9       1017.9       NaN       NaN     12.5   \n",
       "2         78.0       1025.9       1025.3       7.0       8.0      3.9   \n",
       "3         34.0       1025.0       1022.2       NaN       NaN     15.2   \n",
       "4         42.0       1018.0       1014.1       4.0       7.0     19.1   \n",
       "5         82.0       1014.7       1013.6       NaN       NaN     18.7   \n",
       "6          NaN       1023.5       1022.3       6.0       NaN     11.6   \n",
       "7         53.0       1019.7       1015.2       7.0       7.0     16.1   \n",
       "8         60.0       1012.3       1008.6       1.0       7.0     13.2   \n",
       "9         80.0       1022.8       1021.1       1.0       7.0     19.3   \n",
       "\n",
       "   Temp3pm  RainToday  RainTomorrow  \n",
       "0     22.0         No            No  \n",
       "1     23.7         No           Yes  \n",
       "2      9.0        Yes            No  \n",
       "3     21.7         No            No  \n",
       "4     28.2         No            No  \n",
       "5     21.0         No            No  \n",
       "6      NaN        Yes            No  \n",
       "7     19.2         No            No  \n",
       "8     15.9         No           Yes  \n",
       "9     18.5         No            No  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/train.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
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       "      <th>Rainfall</th>\n",
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       "      <th>0</th>\n",
       "      <td>2012/1/19</td>\n",
       "      <td>MountGinini</td>\n",
       "      <td>12.1</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>54.0</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015/4/13</td>\n",
       "      <td>Nhil</td>\n",
       "      <td>10.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>E</td>\n",
       "      <td>39.0</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>63.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1021.9</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010/8/5</td>\n",
       "      <td>Nuriootpa</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>W</td>\n",
       "      <td>28.0</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1025.9</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013/3/18</td>\n",
       "      <td>Adelaide</td>\n",
       "      <td>13.2</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>44.0</td>\n",
       "      <td>E</td>\n",
       "      <td>...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011/2/16</td>\n",
       "      <td>Sale</td>\n",
       "      <td>14.1</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>E</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>92.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>1014.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013/4/16</td>\n",
       "      <td>Walpole</td>\n",
       "      <td>16.1</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>E</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>94.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>1014.7</td>\n",
       "      <td>1013.6</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>21.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016/8/12</td>\n",
       "      <td>Albany</td>\n",
       "      <td>8.5</td>\n",
       "      <td>16.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>1022.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011/8/7</td>\n",
       "      <td>Moree</td>\n",
       "      <td>10.8</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NNE</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1019.7</td>\n",
       "      <td>1015.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.1</td>\n",
       "      <td>19.2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009/9/3</td>\n",
       "      <td>Bendigo</td>\n",
       "      <td>9.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1012.3</td>\n",
       "      <td>1008.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>15.9</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014/10/15</td>\n",
       "      <td>NorfolkIsland</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.2</td>\n",
       "      <td>ENE</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1022.8</td>\n",
       "      <td>1021.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.3</td>\n",
       "      <td>18.5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Location  MinTemp  MaxTemp  Rainfall  Evaporation  \\\n",
       "0   2012/1/19    MountGinini     12.1     23.1       0.0         -1.0   \n",
       "1   2015/4/13           Nhil     10.2     24.7       0.0         -1.0   \n",
       "2    2010/8/5      Nuriootpa     -0.4     11.0       3.6          0.4   \n",
       "3   2013/3/18       Adelaide     13.2     22.6       0.0         15.4   \n",
       "4   2011/2/16           Sale     14.1     28.6       0.0          6.6   \n",
       "5   2013/4/16        Walpole     16.1     21.6       0.0         -1.0   \n",
       "6   2016/8/12         Albany      8.5     16.4       5.0          2.2   \n",
       "7    2011/8/7          Moree     10.8     19.4       0.0          3.8   \n",
       "8    2009/9/3        Bendigo      9.0     20.6       0.0          4.0   \n",
       "9  2014/10/15  NorfolkIsland     14.0     21.1       0.0          6.6   \n",
       "\n",
       "   Sunshine WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  \\\n",
       "0      -1.0           W           30.0          N  ...        60.0   \n",
       "1      -1.0           E           39.0          E  ...        63.0   \n",
       "2       1.6           W           28.0          N  ...        97.0   \n",
       "3      11.0          SE           44.0          E  ...        47.0   \n",
       "4       6.7           E           28.0         NE  ...        92.0   \n",
       "5      -1.0           E           26.0         -1  ...        94.0   \n",
       "6      -1.0          -1           -1.0         NW  ...        78.0   \n",
       "7       1.2         NNE           33.0          N  ...        67.0   \n",
       "8      -1.0         WNW           48.0        NNE  ...        54.0   \n",
       "9       6.2         ENE           37.0         NE  ...        58.0   \n",
       "\n",
       "   Humidity3pm  Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  \\\n",
       "0         54.0         -1.0         -1.0      -1.0      -1.0     17.0   \n",
       "1         33.0       1021.9       1017.9      -1.0      -1.0     12.5   \n",
       "2         78.0       1025.9       1025.3       7.0       8.0      3.9   \n",
       "3         34.0       1025.0       1022.2      -1.0      -1.0     15.2   \n",
       "4         42.0       1018.0       1014.1       4.0       7.0     19.1   \n",
       "5         82.0       1014.7       1013.6      -1.0      -1.0     18.7   \n",
       "6         -1.0       1023.5       1022.3       6.0      -1.0     11.6   \n",
       "7         53.0       1019.7       1015.2       7.0       7.0     16.1   \n",
       "8         60.0       1012.3       1008.6       1.0       7.0     13.2   \n",
       "9         80.0       1022.8       1021.1       1.0       7.0     19.3   \n",
       "\n",
       "   Temp3pm  RainToday  RainTomorrow  \n",
       "0     22.0         No            No  \n",
       "1     23.7         No           Yes  \n",
       "2      9.0        Yes            No  \n",
       "3     21.7         No            No  \n",
       "4     28.2         No            No  \n",
       "5     21.0         No            No  \n",
       "6     -1.0        Yes            No  \n",
       "7     19.2         No            No  \n",
       "8     15.9         No           Yes  \n",
       "9     18.5         No            No  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(-1)\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     82786\n",
       "Yes    23858\n",
       "Name: RainTomorrow, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 利用value_counts函数查看训练集标签的数量\n",
    "pd.Series(df['RainTomorrow']).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 数据探索(可视化描述)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/td/.local/miniconda3/envs/py37_ml/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['MinTemp',\n",
       " 'MaxTemp',\n",
       " 'Rainfall',\n",
       " 'Evaporation',\n",
       " 'Sunshine',\n",
       " 'WindGustSpeed',\n",
       " 'WindSpeed9am',\n",
       " 'WindSpeed3pm',\n",
       " 'Humidity9am',\n",
       " 'Humidity3pm',\n",
       " 'Pressure9am',\n",
       " 'Pressure3pm',\n",
       " 'Cloud9am',\n",
       " 'Cloud3pm',\n",
       " 'Temp9am',\n",
       " 'Temp3pm']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_features = [x for x in df.columns if df[x].dtype == np.float]\n",
    "numerical_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/td/.local/miniconda3/envs/py37_ml/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['Date', 'Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_features = [x for x in df.columns if df[x].dtype != np.float and x != 'RainTomorrow']\n",
    "category_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 617.875x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 选取三个特征与标签组合的散点可视化\n",
    "sns.pairplot(data=df[['Rainfall',\n",
    "'Evaporation',\n",
    "'Sunshine'] + ['RainTomorrow']], diag_kind='hist', hue= 'RainTomorrow')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2880 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(50,40))\n",
    "\n",
    "for index,col in enumerate(df[numerical_features].columns):\n",
    "    if col != 'RainTomorrow':\n",
    "        plt.subplot(4,4,index+1)\n",
    "        sns.boxplot(x='RainTomorrow', y=col, saturation=0.5, palette='pastel', data=df)\n",
    "        plt.title(col)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "tlog = {}\n",
    "for i in category_features:\n",
    "    tlog[i] = df[df['RainTomorrow'] == 'Yes'][i].value_counts()\n",
    "flog = {}\n",
    "for i in category_features:\n",
    "    flog[i] = df[df['RainTomorrow'] == 'No'][i].value_counts()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(tlog['Location']).sort_index()['Location'], y = pd.DataFrame(tlog['Location']).sort_index().index, color = \"red\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Not RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(flog['Location']).sort_index()['Location'], y = pd.DataFrame(flog['Location']).sort_index().index, color = \"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,2))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(tlog['RainToday'][:2]).sort_index()['RainToday'], y = pd.DataFrame(tlog['RainToday'][:2]).sort_index().index, color = \"red\")\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('Not RainTomorrow')\n",
    "sns.barplot(x = pd.DataFrame(flog['RainToday'][:2]).sort_index()['RainToday'], y = pd.DataFrame(flog['RainToday'][:2]).sort_index().index, color = \"blue\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindSpeed9am</th>\n",
       "      <th>WindSpeed3pm</th>\n",
       "      <th>Humidity9am</th>\n",
       "      <th>Humidity3pm</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.1</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1021.9</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1025.9</td>\n",
       "      <td>1025.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.2</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>1022.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>15.2</td>\n",
       "      <td>21.7</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.1</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>1014.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>28.2</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106639</th>\n",
       "      <td>10.1</td>\n",
       "      <td>16.1</td>\n",
       "      <td>15.8</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>999.2</td>\n",
       "      <td>995.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106640</th>\n",
       "      <td>19.3</td>\n",
       "      <td>31.7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1013.8</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106641</th>\n",
       "      <td>17.5</td>\n",
       "      <td>22.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1008.2</td>\n",
       "      <td>1008.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>21.4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106642</th>\n",
       "      <td>17.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106643</th>\n",
       "      <td>16.3</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1017.9</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.2</td>\n",
       "      <td>35.7</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>106644 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  WindGustSpeed  \\\n",
       "0          12.1     23.1       0.0         -1.0      -1.0           30.0   \n",
       "1          10.2     24.7       0.0         -1.0      -1.0           39.0   \n",
       "2          -0.4     11.0       3.6          0.4       1.6           28.0   \n",
       "3          13.2     22.6       0.0         15.4      11.0           44.0   \n",
       "4          14.1     28.6       0.0          6.6       6.7           28.0   \n",
       "...         ...      ...       ...          ...       ...            ...   \n",
       "106639     10.1     16.1      15.8         -1.0      -1.0           31.0   \n",
       "106640     19.3     31.7      36.0         -1.0      -1.0           80.0   \n",
       "106641     17.5     22.2       1.2         -1.0      -1.0           65.0   \n",
       "106642     17.6     27.0       3.0         -1.0      -1.0           -1.0   \n",
       "106643     16.3     37.9       0.0         14.2      12.2           41.0   \n",
       "\n",
       "        WindSpeed9am  WindSpeed3pm  Humidity9am  Humidity3pm  Pressure9am  \\\n",
       "0                6.0          11.0         60.0         54.0         -1.0   \n",
       "1               13.0           9.0         63.0         33.0       1021.9   \n",
       "2                4.0          20.0         97.0         78.0       1025.9   \n",
       "3               15.0          15.0         47.0         34.0       1025.0   \n",
       "4                4.0          19.0         92.0         42.0       1018.0   \n",
       "...              ...           ...          ...          ...          ...   \n",
       "106639           4.0           7.0         99.0         86.0        999.2   \n",
       "106640          17.0          30.0         75.0         76.0       1013.8   \n",
       "106641          39.0          19.0         61.0         56.0       1008.2   \n",
       "106642           6.0          15.0         68.0         88.0         -1.0   \n",
       "106643          20.0          13.0          8.0          6.0       1017.9   \n",
       "\n",
       "        Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm RainTomorrow  \n",
       "0              -1.0      -1.0      -1.0     17.0     22.0           No  \n",
       "1            1017.9      -1.0      -1.0     12.5     23.7          Yes  \n",
       "2            1025.3       7.0       8.0      3.9      9.0           No  \n",
       "3            1022.2      -1.0      -1.0     15.2     21.7           No  \n",
       "4            1014.1       4.0       7.0     19.1     28.2           No  \n",
       "...             ...       ...       ...      ...      ...          ...  \n",
       "106639        995.2      -1.0      -1.0     13.0     15.6          Yes  \n",
       "106640       1010.0      -1.0      -1.0     26.0     25.8          Yes  \n",
       "106641       1008.2      -1.0      -1.0     17.8     21.4           No  \n",
       "106642         -1.0       6.0       5.0     22.6     26.4           No  \n",
       "106643       1014.0       0.0       1.0     32.2     35.7           No  \n",
       "\n",
       "[106644 rows x 17 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[numerical_features+['RainTomorrow']]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 利用 XGBoost 进行训练与预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 为了正确评估模型性能，将数据划分为训练集和测试集，并在训练集上训练模型，在测试集上验证模型性能。\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "## 选择其类别为0和1的样本 （不包括类别为2的样本）\n",
    "data_target_part = data['RainTomorrow']\n",
    "data_features_part = data[[x for x in data.columns if x != 'RainTomorrow']]\n",
    "\n",
    "## 测试集大小为20%， 80%/20%分\n",
    "x_train, x_test, y_train, y_test = train_test_split(data_features_part, data_target_part, test_size = 0.2, random_state = 2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 导入XGBoost模型\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "## 定义 XGBoost模型 \n",
    "clf = XGBClassifier()\n",
    "# 在训练集上训练XGBoost模型\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 模型预测 & 评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 准确度评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 在训练集和测试集上分布利用训练好的模型进行预测\n",
    "train_predict = clf.predict(x_train)\n",
    "test_predict = clf.predict(x_test)\n",
    "from sklearn import metrics\n",
    "\n",
    "## 利用accuracy（准确度）【预测正确的样本数目占总预测样本数目的比例】评估模型效果\n",
    "print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_train,train_predict))\n",
    "print('The accuracy of the Logistic Regression is:',metrics.accuracy_score(y_test,test_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 查看混淆矩阵 (预测值和真实值的各类情况统计矩阵)\n",
    "confusion_matrix_result = metrics.confusion_matrix(test_predict,y_test)\n",
    "print('The confusion matrix result:\\n',confusion_matrix_result)\n",
    "\n",
    "# 利用热力图对于结果进行可视化\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_matrix_result, annot=True, cmap='Blues')\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.show()"
   ]
  }
 ],
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